Genome-Wide Association Study for Certain Carcass Traits and Organ Weights in a Large White×Minzhu Intercross Porcine Population

Journal of Integrative Agriculture 2014, 13(12): 2721-2730

December 2014

RESEARCH ARTICLE

Genome-Wide Association Study for Certain Carcass Traits and Organ Weights in a Large White×Minzhu Intercross Porcine Population LIU Xin1*, WANG Li-gang1*, LIANG Jing1, YAN Hua1, ZHAO Ke-bin1, LI Na1, 2, ZHANG Long-chao1 and WANG Li-xian1 1

Key Laboratory of Farm Animal Genetic Resources and Germplasm Innovation, Ministry of Agriculture/Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P.R.China 2 Jilin Academy of Agricultural Sciences, Changchun 130033, P.R.China

Abstract Porcine carcass traits and organ weights have important economic roles in the swine industry. A total of 576 animals from a Large White×Minzhu intercross population were genotyped using the Illumina PorcineSNP60K Beadchip and were phenotyped for 10 traits, specifically, backfat thickness (6-7 libs), carcass length, carcass weight, foot weight, head weight, heart weight, leaf fat weight, liver weight, lung weight and slaughter body weight. The genome-wide association study (GWAS) was assessed by Genome Wide Rapid Association using the mixed model and regression-genomic control approach. A total of 31 single nucleotide polymorphisms (SNPs) (with the most significant SNP being MARC0033464, P value=6.80×10-13) were located in a 9.76-Mb (31.24-41.00 Mb) region on SSC7 and were found to be significantly associated with one or more carcass traits and organ weights. High percentage of phenotypic variance explanation was observed for each trait ranging from 31.21 to 67.42%. Linkage analysis revealed one haplotype block of 495 kb, in which the most significant SNP being MARC0033464 was contained, on SSC7 at complete linkage disequilibrium. Annotation of the pig reference genome suggested 6 genes (GRM4, HMGA1, NUDT3, RPS10, SPDEF and PACSIN1) in this candidate linkage disequilibrium (LD) interval. Functional analysis indicated that the HMGA1 gene presents the prime biological candidate for carcass traits and organ weights in pig, with potential application in breeding programs. Key words: genome-wide association study (GWAS), carcass trait, HMGA1 gene, organ weight, pig

INTRODUCTION Carcass traits have important economic roles in the pig industry. In the past, the analyses of quantitative trait loci (QTL), which are detected from linkage maps and data (Malek et al. 2001; Ma et al. 2013), have been primarily used to identify important livestock traits and

a number of QTLs having been reported for porcine carcass traits. QTLs affecting weights of internal organs were intensively investigated by genome scans and confirmed many QTLs on SSC1, 2, 4, 5, 7 and 8 (Ma et al. 2009). Important QTLs located on chromosome 7 were associated with body conformation, fat deposition and organ weight (Yue et al. 2003). Today, over 8 000 QTLs, representing ~630 different traits, have been identified via genome scans and

Received 10 February, 2014 Accepted 16 May, 2014 LIU Xin, E-mail: [email protected]; Correspondence WANG Li-xian, Tel: +86-10-62816011, Fax: +86-10-62818771, E-mail: [email protected]; ZHANG Longchao, Tel: +86-10-62816011, E-mail: [email protected] * These authors contributed equally to this study. © 2014, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(14)60787-5

LIU Xin et al.

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released in the pig QTLdb (http://www.animalgenome. org/cgi-bin/QTLdb/SS/index). However, due to the large interval of QTLs, only a few quantitative trait nucleotides (QTN) have been identified in pig production by the fine-scale mapping of QTLs (van Laere et al. 2003; Ren et al. 2011). Hence, this approach is subject to limitations. Through the efforts of genome sequencing projects worldwide, and the development of high-throughput genotyping platforms, genome-wide association studies (GWAS) have been widely applied to several species to survey most of the genome using genetic variants (Hirschhorn and Daly 2005). For domestic animals, GWAS have been mainly focused on production traits (Goddard and Hayes 2009) which may be used in marker-assisted selection (MAS). Up to now, numerous GWAS results have been reported in the field of domestic animals, e.g., milk production and growth traits in dairy cattle (Jiang et al. 2010; Bolormaa et al. 2011), body composition, meat quality and egg quality in chickens (Liu et al. 2013). In pigs, various commercial traits, including meat quality, hematological parameters, reproduction and body composition (Fan et al. 2011; Luo et al. 2012; Onteru et al. 2012), have been reported, however, the GWAS for porcine carcass traits remains limited. The available 60K single nucleotide polymorphism (SNP) porcine panel is dense, providing an opportunity to locate the candidate SNPs or genes. In the present study, a GWAS was performed to detect potential genetic variants associated with certain carcass traits and organ weights using a Large White×Minzhu intercross porcine population.

RESULTS GWAS After the quality control procedure, 48 238 SNPs and 506 F2 individuals were used for the genome-wide association studies. The selected SNPs were distributed on 18 autosomes and the X/Y chromosome, as shown in Table 1. Ten traits were assessed in GWAS, with Manhattan plots for 7 (significant association) of 10 traits shown in Fig. 1. The other Manhattan plots and quantile-quantile (Q-Q) plots were displayed in Ap-

Table 1 Distribution of single nucleotide polymorphisms (SNPs) after quality control and the average distances between SNPs on each chromosome Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 X Y 02) Total 1) 2)

No. SNPs 5 155 2 122 1 659 2 903 1 776 1 505 2 838 1 770 2 080 1 094 1 478 893 2 860 3 150 2 025 1 264 1 314 901 668 1 10 792 48 238

Average distance (kb)1) 61.17 76.61 87.27 49.42 62.79 104.83 47.49 83.89 73.88 72.31 59.33 71.21 76.45 48.84 77.87 68.75 53.05 67.95 216.00

Derived from Sus scrofa Build 10.2. These SNPs are not assigned to any chromosomes.

pendixes A and B, respectively. The Q-Q plot results indicated that the large linkage disequilibrium (LD) in F2 population could bring about some certain degree deviations between the predicted and real data. No single nucleotide polymorphism (SNP) for carcass weight (CW), heart weight (HTW) and lung weight (LUW) reached genome-wide significant levels. In the GWAS, a total of 31 genome-wide significant SNPs were located in a 9.76-Mb (31.24-41.00 Mb) region on SSC7. Of the 31 SNPs, 8, 31, 30, 26, 9, 7 and 8 SNPs were significantly associated with backfat thickness (6-7 libs) (BFT), carcass length (CL), foot weight (FW), head weight (HW), leaf fat weight (LFW), liver weight (LW) and slaughter body weight (SBW) (Tables 2 and 3). The most significant SNP for each trait was MARC0033464 located on chromosome 735 177 641 bp, which explained 37.11, 51.76, 67.42, 53.24, 36.87, 31.21 and 33.33% of the phenotypic variance for BFT, CL, FW, HW, LFW, LW and SBW, respectively. All significant SNPs were blasted on ENSEMBL (http:// asia.ensembl.org/index.html), with 14 SNPs being located within 9 known genes, specifically, 7SK snRNA methylphosphate capping enzyme-like (7SK), primase DNA polypeptide 2 (PRIM2), dystonin (DST), collagen type IX alpha 3 (COL9A3), ubiquitin-like with PHD and ring finger domains 1 binding protein 1 (UHRF1BP1), © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

-log10(observed value)

9 8 7 6 5 4 3 2 1 0

-log10(observed value)

-log10(observed value)

-log10(observed value)

-log10(observed value)

-log10(observed value)

9 8 7 6 5 4 3 2 1 0

-log10(observed value)

Genome-Wide Association Study for Certain Carcass Traits and Organ Weights in a Large White×Minzhu Intercross Porcine

2723

BFT

CL

FW

9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0

HW

9 8 7 6 5 4 3 2 1 0

LFW

9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0

LW

SBW

1

2

3

4

5

6

7 8 9 10 11 12 13 Chromosome

14

15 16 17 18 x

Fig. 1 Manhattan plots of genome-wide association study with 7 traits. Chromosomes 1-18 and X are shown separated by color. The red horizontal lines indicate the genome-wide significance levels (-log10(2.07E-08)).

ankyrin repeat and sterile alpha motif domain containing

delta (PPARD) and solute carrier family 26 member 8

1A (ANKS1A), signal peptide CUB domain EGF-like 3

(SLC26A8). All other SNPs were mapped from 5 782

(SCUBE3), peroxisome proliferator-activated receptor

to 82 751 bp away from the nearest known genes.

© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

32 745

Within

Within

5 782

Within

38 479

Within

18 523

82 751

35 677

10 110

24 945

33 110

36 724

7 190

30 460

6 561

Within

Within

Within

Within

Within

Within

Within

Within

Within

43 389

19 027

Within

51 618

KLHL31

7SK

PRIM2

RAB23

DST

DST

COL9A3

MNF1

MLN

GRM4

231603）

231603)

RPS10

SPDEF

PACSIN1

C6ORF106

C6ORF106

UHRF1BP1

ANKS1A

ANKS1A

SCUBE3

SCUBE3

SCUBE3

SCUBE3

PPARD

PPARD

FKBP5

FKBP5

SLC26A8

LRFN2

31 628 041

32 267 617

32 957 770

33 086 096

33 740 960

33 790 291

33 991 471

34 556 148

34 755 605

34 803 564

35 002 839

35 017 674

35 150 546

35 177 641

35 251 333

35 332 375

35 356 274

35 530 335

35 579 961

35 709 335

35 880 196

35 935 629

35 959 385

36 004 578

36 169 892

36 202 231

36 329 680

36 497 507

36 684 494

41 004 533

DRGA0007448

MARC0077640

ASGA0032302

ASGA0032313

ALGA0040120

H3GA0020692

ALGA0040148

H3GA0020739

H3GA0020765

MARC0058766

ALGA0040260

ALGA0040263

ASGA0032536

MARC0033464

ASGA0032526

H3GA0020824

ASGA0032549

ASGA0032562

INRA0024805

ASGA0032571

M1GA0010006

MARC0039836

H3GA0020842

H3GA0020849

ASGA0032583

H3GA0020846

INRA0024809

ASGA0032595

ALGA0040331

ALGA0040717

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

P value

4.29E-09

1.09E-08

5.71E-12

1.09E-08

9E-09

9E-09

6.98E-12

1.77E-11

6.87E-12

3.62E-09

2.02E-09

1.7E-09

2.02E-09

3.68E-09

3.68E-09

7.18E-09

1.27E-12

7.18E-09

7.18E-09

7.18E-09

5.34E-12

6.18E-12

1.18E-11

8.01E-10

6.7E-10

1.52E-09

2.36E-09

3.2E-09

6.98E-12

4.02E-09

1.09E-08

39.75

35.88

49.19

35.88

36.06

36.06

48.77

49.10

48.82

36.81

36.87

37.30

36.87

37.03

37.03

36.18

51.76

36.22

36.17

36.17

50.50

50.54

48.80

45.22

44.28

43.21

39.65

39.79

48.77

39.48

38.37

Var (%)2)

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

FW

　

FW

Trait

P value

1.47E-08

6.04E-09

7.31E-12

6.04E-09

4.91E-09

4.91E-09

1.71E-11

1.08E-10

1.72E-11

1.96E-09

6.29E-10

5.81E-10

6.29E-10

2.45E-09

2.45E-09

1.89E-09

6.80E-13

1.89E-09

1.89E-09

1.89E-09

7.75E-12

6.30E-12

1.06E-11

5.30E-10

5.65E-10

2.14E-09

1.19E-09

1.73E-09

1.71E-11

3.79E-09

51.00

47.81

64.15

47.81

48.13

48.13

62.88

62.97

62.90

48.87

49.00

49.03

49.00

48.93

48.93

48.67

67.42

48.68

48.70

48.70

65.62

65.74

63.64

59.59

58.51

56.63

53.78

53.91

62.88

52.28

Var (%)2)

　

　

HW

　

　

　

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

HW

Trait

CL, arcass length; FW, foot weight; HW, head weight; SBW, slaughter body weight; BFT, backfat thickness (6-7 libs). The same as below. 2) Phenotypic variation explained by the SNP. The same as below. 3) Gene name is ENSSSCG00000023160 in ENSEMBL database.

1)

CL

65 107

LRRC1

31 237 418

ALGA0039921 CL

Trait

Nearest gene Distance (bp)

Position (bp)

SNP

Table 2 Genome-wide significant SNPs on SSC7 associated with 5 traits1） P value

1.34E-10

2.03E-10

1.36E-09

1.87E-10

7.29E-09

2.34E-09

2.41E-09

2.34E-09

9.27E-09

9.27E-09

1.87E-08

3.32E-11

1.87E-08

1.87E-08

1.87E-08

3.17E-10

2.35E-10

3.06E-10

7.83E-09

2.81E-09

1.06E-08

7.03E-09

7.33E-09

2.03E-10

5.63E-09

1.70E-08

52.04

51.39

51.35

51.59

42.05

42.38

42.27

42.38

41.98

41.98

40.76

53.24

40.76

40.73

40.73

51.70

51.77

50.38

48.44

49.17

47.72

44.93

45.67

51.39

45.29

43.98

Var (%)2)

SBW

SBW

SBW

SBW

SBW

SBW

SBW

　

　

　

　

　

SBW

　

　

Trait

6.48E-07

4.07E-07

3.41E-07

1.74E-07

8.03E-07

5.51E-07

8.28E-07

4.07E-07

P value

32.10

32.14

32.55

33.33

32.22

32.18

31.79

32.14

Var (%)2)

　

BFT

　

　

BFT

BFT

　

　

　

BFT

　

　

BFT

BFT

BFT

　

　

　

BFT

Trait

　

1.51E-09

　

　

7.23E-09

8.13E-09

　

　

　

3.05E-10

　

　

2.24E-09

2.04E-09

6.19E-09

　

　

　

　

7.23E-09

P value

36.67

35.99

36.07

37.11

37.11

37.32

36.20

35.99

Var (%)2)

2724 LIU Xin et al.

© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

Genome-Wide Association Study for Certain Carcass Traits and Organ Weights in a Large White×Minzhu Intercross Porcine

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Table 3 Genome-wide significant SNPs on SSC7 associated with 2 traits1) SNP MARC0077640 H3GA0020739 H3GA0020765 MARC0058766 MARC0033464 MARC0039836 H3GA0020842 H3GA0020849 ASGA0032595 1)

Position (bp) 32 267 617 34 556 148 34 755 605 34 803 564 35 177 641 35 935 629 35 959 385 36 004 578 36 497 507

Nearest gene 7SK MNF1 MLN GRM4 SPDEF SCUBE3 SCUBE3 SCUBE3 FKBP5

Distance (bp) Within 18 523 82 751 35 677 36 724 Within Within Within 19 027

Trait LFW LFW LFW LFW LFW LFW LFW LFW LFW

P value 1.57E-09 5.94E-10 2.74E-10 3.08E-10 5.14E-11 1.50E-09 1.45E-08 1.57E-09 5.44E-10

Var (%) 36.02 36.66 37.75 37.70 36.87 36.03 36.55 36.02 36.02

Trait LW LW LW LW LW LW

P value 7.60E-09 9.53E-09 3.96E-09 6.50E-09 2.54E-09 7.92E-09

Var (%) 29.75 30.29 30.95 30.72 31.21 29.76

LW

7.60E-09

29.75

LFW, leaf fat weight; LW, liver weight. The same as below.

Conditioned analysis A conditioned analysis was performed using the most significant SNP MARC0033464, as a fixed effect. The Manhattan plots obtained from the conditioned analysis are shown in Appendix C, respectively. There was no significant SNP on SSC7 after the conditioned analysis. However, 12 SNPs in a 1.30-Mb (85.03-86.33 Mb) region on SSC4 showed chromosome-wide association with CL, FW and HW (Appendix D), when the lighter conventional Bonferroni chromosome-wide significant P value of 1.22×10-5 was employed. This region contained 2 annotated gene, which may represent potential candidate genes for further study, namely, suppression of tumorigenicity 18 (ST18) and protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 1 (PCMTD1).

Haplotype association analysis Linkage analysis in the 9.76-Mb significant region identified 3 haplotype blocks from the 124 kb to the 495 kb regions (Fig. 2). However, block 1 at 495 kb (from H3GA0020765 to ASGA0032526) was at complete LD (r2=1) and contained the most significant SNP MARC0033464. Haplotype frequencies were calculated and association analysis was performed for block 1 with CL, which was associated with 31 of the significant SNPs (Table 4). The global score P value for CL was <10-5. The AAAGCAG (25.79%, with a positive effect) and CGGAAGA (56.35%, with a negative effect) haplotypes were significantly associated (P<1E-5) with CL. The 495 kb haplotype included 6 annotated genes, namely, glutamate receptor, metabotropic 4 (GRM4), high mobility group AT-hook 1 (HMGA1), nudix (nucleoside diphos-

phate linked moiety X)-type motif 3 (NUDT3), ribosomal protein S10 (RPS10), SAM pointed domain containing ETS transcription factor (SPDEF), and protein kinase C and casein kinase substrate in neurons 1 (PACSIN1).

DISCUSSION Candidate regions for carcass traits and organ weights In the present study, a genome-wide association study was performed for 10 traits to identify significant SNPs in porcine. The most significant SNPs in the GWAS were located in proximal regions from 31.24 to 41.00 Mb on SSC7. These SNPs, which were associated with each trait, were located within known QTL regions. One QTL could influence a several traits and most biological traits also have a multifactorial (or complex) inheritance, which indicates that they are influenced by numerous genes (Anderson and Georges 2004). This GWAS shows that there are several SNPs mapped in a 9.76 Mb (31.24-41.00 Mb) segment on SSC7 affecting both carcass traits and organ weights, which suggest it should be a QTL with pleiotropic effects affecting the mentioned traits. Similar to our results, SSC7 has been previously shown to be rich in QTL influencing carcass composition traits (Paszek et al. 2001; Milan et al. 2002; Gilbert et al. 2007). The region from 31.27 to 58 Mb on SSC7 has been previously shown to have pleiotropic and significant QTL effects on carcass weight, internal organ weight and meat quality traits measured in the White Duroc×Erhualian F2 intercross population (Ma et al. 2013). The results of this preceding GWAS narrowed down the QTL to a 9.76-Mb (31.24-41.00 Mb) region on SSC7. However, due to the strong linkage disequi© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

LIU Xin et al.

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Fig. 2 Haplotype block at linkage disequilibrium (LD) on a 9.76-Mb region on SSC7 containing all the significant SNPs. Solid lines mark three blocks identified. The block 1 of 495 kb, which contained the most significant SNP MARC0033464, was at complete linkage disequilibrium (LD, r2=1). Table 4 Haplotype association analysis of block 1 with carcass length Trait

Haplotype Hap-Freq1) Hap-score2)

Carcass CGGAAGA length AAAGCGG CGAGCGG AAAGCAG

0.56 0.02 0.16 0.26

-9.27 -0.42 -0.20 11.12

Haplotype-specific Global score score P value3) statistic4) <1×10-5 χ2=127.50 df=3 0.67 P value 0.84 <1×10-5 <1×10-5

1)

Hap-Freq, estimated frequency of each haplotype in the population. Hap-score, the score for the haplotype, which is the statistical measurement of association of each specific haplotype with the trait. 3) Haplotype-specific score P value, the asymptotic chi-square P value was calculated from the square of the score statistic. 4) Global score statistic, the overall association between haplotypes and the response. 2)

librium occurred in the F2 design population, more SNPs in larger regions associated with given traits, e.g., BFT were detected than from the pure line population (Okumura et al. 2013). The large linkage disequilibrium in intercross population is one of the limits well known and will bring more difficult to identify the causative gene and mutation. In addition, the single SNP association, in which the linkage of multiple SNPs was not considered, is another reason to detect more significant SNPs in this GWAS. Although the simpleM method was applied in this work, only the number of effective SNPs, instead of effective SNPs, could be estimated in this GWAS. Furthermore, the identical-by-descent (IBD) analysis in different type breeds could identify the minimum IBD haplotype to narrow down the QTL region (Anderson and Georges 2004) and will be applied in our future studies. In the present study, the significant effect of several SNPs disappeared in the conditioned analysessuggest that the causative mutation is in a higher linkage disequilibrium with the SNP included as fixed factor (MARC0033464) than with the other. A high percentage of phenotypic variance explanation always exists in the

specific F2 design populations, which are constructed by 2 extreme distinct breeds. The reported QTLs on SSC7 explained a high percentage of phenotypic variance in F2 populations, representing about 28, 42 and 58% for of leaf fat weight, head weight and feet weight, respectively (Milan et al. 2002; Ma et al. 2009). Supporting previous reports, a high percentage of phenotypic variance was also obtained for each trait in the current work, ranging from 31.21 to 67.42%. Even phenotypic variances explained were over 50% for carcass length, feet weight and head weight. The results suggested that QTLs have a major effect on carcass traits and organ weights in this 9.76-Mb region on SSC7.

HMGA1 as a good candidate gene Linkage analysis revealed 495 kb haplotype block in the significant region on SSC7 at complete linkage disequilibrium, which contained 6 annotated genes of the pig reference genome. Growth and fatness QTL on pig chromosome 7 has been demonstrated (Rothschild 2004). Of the 6 genes, HMGA1 gene has been mapped to the peak of the fat deposition QTL on pig chromosome 7 and HMGA1 polymorphisms were associated with fat deposition traits across several pig population (Kim et al. 2004, 2006). HMGA1 is ubiquitous in all the cells of higher eukaryotes (Cleynen and van de Ven 2008), with the HMGA1 protein having a biological role in cell growth and differentiation (Melillo et al. 2001). Although widely expressed during embryonic development, its expression level is negligible or absent in fully differentiated adult tissues (Chiappetta et al. 1996). The HMGA1 protein is overexpressed in >90% of primary © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

Genome-Wide Association Study for Certain Carcass Traits and Organ Weights in a Large White×Minzhu Intercross Porcine

pancreatic cancers, and is absent or present at low levels in early precursor lesions (Hristov et al. 2010). Silencing HMGA1 expression in invasive, aggressive cancer cells dramatically halts cell growth and blocks oncogenic properties, including proliferation, migration, invasion, and orthotopic tumorigenesis (Shah et al. 2013). By influencing the expression of 2 IGFBP protein species, HMGA1 serves as a modulator of IGF-I activity (Iiritano et al. 2012). In dogs, a single IGF1 single-nucleotide polymorphism haplotype is common to all small breeds and nearly absent from giant breeds, indicating that the same causal sequence variant is a major contributor to body size (Sutter et al. 2007). In addition, in human studies, some GWAS have reported that HMGA1 may represent a good candidate for anthropometric traits (Berndt et al. 2013). For instance, HMGA1 may modulate IGF-I activity to influence body growth, hence, it might also be the prime candidate for carcass traits and organ weights in pigs.

CONCLUSION In summary, this study identified a total of 31 significant SNPs associated with 1 or more carcass traits and organ weights in a 9.76-Mb (31.24-41.00 Mb) region on SSC7. Conditioned analysis confirmed that significant SNPs originated from a single QTL. Furthermore linkage analysis refined the QTL to a 495-kb complete linkage disequilibrium region containing the HMGA1 gene. Exploration of the gene at associated loci through additional genetic, functional, and computational studies is expected to lead to novel insights into carcass traits and organ weights in different pig breeds.

2727

Science, Chinese Academy of Agricultural Sciences (CAAS) (Beijing, China).

Animals and phenotypic data A 3-generation resource population was used in this study. This population was constructed by intercrossing Large White boars and Minzhu sows between 2007 and 2011. Four Large White boars were mated with 16 Minzhu sows to produce an F1 population. Nine boars and 46 sows, all from the F1 population, were intercrossed to produce an F2 population containing 576 animals. All the animals were reared under the same nutritional and environmental conditions, and were housed at Institute of Animal Science, CAAS. According to standard commercial procedures, all F2 animals were slaughtered in 28 batches at (240±7) d in the commercial abattoir. Ten traits were measured, specifically, backfat thickness (6-7 libs) (BFT), carcass length (CL), carcass weight (CW), foot weight (FW), head weight (HW), heart weight (HTW), leaf fat weight (LFW), liver weight (LW) , lung weight (LUW) and slaughter body weight (SBW). Descriptive statistics including means, standard deviations, minimum, maximum and coefficient of variation of the F2 individuals are presented in Table 5. The means for BFT, CL, CW, FW, HW, HTW, LFW, LW, LUW and SBW were 38.38 mm, 95.58 cm, and 78.96, 1.69, 7.52, 0.33, 1.46, 1.32, 0.61 and 109.11 kg, respectively. Table 5 Descriptive statistics of phenotypic data Traits1) BFT (mm) CL (cm) CW (kg) FW (kg) HW (kg) HTW (kg) LFW (kg) LW (kg) LUW (kg) SBW (kg ) 1)

Mean 38.38 95.58 78.96 1.69 7.52 0.33 1.46 1.32 0.61 109.11

Standard deviation 8.06 6.18 12.21 0.39 1.28 0.06 0.56 0.23 0.16 15.93

Minimum 14.99 78.00 42.00 0.92 4.1 0.20 0.30 0.12 0.35 64.40

Maximum 62.87 118.50 120.40 2.83 11.58 0.57 3.54 2.11 1.47 156.40

CV 21.01 6.47 15.47 22.94 17.06 18.15 38.45 17.56 26.78 14.60

CW, carcass weight; HTW, heart weight; LUW, lung weight.

MATERIALS AND METHODS

Genotyping and quality control

Ethics statement

Genomic DNA was isolated and extracted from the ear or longissimus dorsi using the phenol-chloroform method, and then diluted to 50 ng μL-1. Genotyping was performed using the Illumina PorcineSNP60 Genotyping BeadChip technology, which contained 62 163 SNPs across the whole genome. All F2 animals were genotyped by BEADSTUDIO software (Illumina, USA). Pedigree mismatching was checked by the Cervus program (Marshall et al. 1998) using SNP information before quality control. F2 animals were quality controlled by

All animal procedures were performed according to the guidelines developed by the China Council on Animal Care, and all protocols were approved by the Animal Care and Use Committee of Beijing, China. The approval ID or permit numbers were SYXK (Beijing) 2008-007 and SYXK (Beijing) 2008-008. Animal experiments were approved by the Science Research Department of the Institute of Animal

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the GenABEL package (Aulchenko et al. 2007) within the R statistical environment. In the quality control procedure, specific criteria were followed, with a call rate <90%, minor allele frequency (MAF) <3%, and Hardy-Weinberg equilibrium (HWE) values (P<10-6). A total of 506 F2 animals were used for GWAS after the quality control.

Statistical analysis The association analysis was conducted via genome-wide rapid association using the mix model and regression-genomic control (GRAMMAR-GC) method (Amin et al. 2007; Aulchenko et al. 2007). In the first step, the mixed model was used, and data were analyzed using DMU software (Madsen et al. 2006), using the formula: y=1µ+Xb+pw+Tc+Za+e Where, y is the vector of the phenotypes of the 576 F2 individuals, b is the vector of fixed effects consisting of the sex, parity and batch, w is the vector of the body weights of the individuals as a covariate, c is the vector of litter effect as a random effect, c~N(0, σc2), a is the vector of random additive genetic effects with a~N(0, Aσc2) (where A is the relationship matrix calculated from the corrected pedigree and σc2 is the additive genetic variance). X, T and Z are incidence matrices related to records of fixed and random effects in y, and p is the regression coefficient of body weight, e is the vector of residual errors, e~N(0, Iσe2) where I is the identity matrix and σe2 is the residual variance. The vector of residuals y* is estimated as: ^ ^ ^ ^ ^ y*=y-(1µ+Xb+pw+Tc+Za) ^ ^ ^ ^ Where, b, p, c, and a are estimates and predictors for b, p, c and a, respectively. Second, the residuals were used as the dependent trait, via the formula: y*=1µ+kg+e* Where, y* is the vector of the adjusted phenotypes in the first step, g is the vector of the genotypes, k is the regression coefficient, and e* is the vector of random residuals. Based on single locus regression analysis, the analysis was performed in the R statistical environment using the GenABEL package. Finally, the unadjusted test statistic factor of the ith SNP Ti2 was calculated in the genomic control (GC) procedure as: ^ ^ Ti2 = k i2/var(k i) ^ ^ Where, ki and var(ki) are the estimate and sample variance of k, respectively. The deflation factor λ is estimated as λ=Median(T12, T22, …, Ti2)/0.456, where 0.456 is the median of χ(1)2 (Amin et al. 2007). The association of the ith SNP with ^ the trait was examined by comparison of T12/λ with χ(1)2. Data were analyzed by the GenABEL package. Adding the most significant SNP as a fixed effect, conditioned analysis was performed by following the state GWAS procedure. The Bonferroni method was conducted for the genome-wide significant threshold, in which the conventional P value was divided by the number of tests performed. The significant

threshold was 2.07×10-8 (0.001/48 238). The number of genome-wide effective SNPs, which was estimated using a simpleM method (Gao et al. 2008), was 12 039 (Appendix E). A lower conventional Bonferroni P value was calculated as 4.15×10-6 (0.05/12 039) and was applied to avoid missing QTLs. The influence of population stratification was assessed in a quantile-quantile (Q-Q) plot. The Q-Q plot was constructed within the R statistical environment.

Haplotype association analysis Haplotype block detection was performed on the chromosomal region which contained all of the SNPs that were significantly associated with the selected traits. The genotypes of significant SNP loci for 506 F2 individuals and their parents were used to detect the haplotype blocks. The HAPLOVIEW V3.31 program (Barrett et al. 2005) was used to detect and visualize the haplotype blocks. The procedure was run with default parameters following the manual for the HAPLOVIEW program. Association analysis of the detected haplotype blocks and traits of 506 F2 individuals were performed using the Haplo. Stats package (Schaid et al. 2002) within the R statistical environment. A score for each haplotype (hap-score) was calculated and P value was also calculated for the significance of each hap-score. A positive/negative score for a particular haplotype indicated that a haplotype is associated with increased/decreased risk of a given trait. The global score statistic index, which has an asymptotic distribution with degrees of freedom (df) and the P value, was calculated to test overall associations among haplotype blocks and traits.

Acknowledgements

We thank Dr. Gao Huijiang, Institute of Animal Science, Chinese Academy of Agricultural Sciences, for the application of the simpleM method to estimate the number of genome-wide effect SNPs. This research was supported by the Agricultural Science and Technology Innovation Program, China (ASTIPIAS02), the National Key Technology R&D Program of China (2011BAD28B01), the National Natural Science Foundation of China (31201781), the Earmarked Fund for Modern Agroindustry Technology Research System, National Technology Program of China (2011ZX08006-003) and the Chinese Academy of Agricultural Sciences Foundation (2011cj-5, 2012ZL069 and 2014ywf-yb-8). Appendix associated with this paper can be available on http://www.ChinaAgriSci.com/V2/En/appendix.htm

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